env.example

### This is sample file of .env

### Server Configuration
# HOST=0.0.0.0
# PORT=9621
# WORKERS=2
# CORS_ORIGINS=http://localhost:3000,http://localhost:8080
WEBUI_TITLE='Graph RAG Engine'
WEBUI_DESCRIPTION="Simple and Fast Graph Based RAG System"

### Optional SSL Configuration
# SSL=true
# SSL_CERTFILE=/path/to/cert.pem
# SSL_KEYFILE=/path/to/key.pem

### Directory Configuration (defaults to current working directory)
# WORKING_DIR=<absolute_path_for_working_dir>
# INPUT_DIR=<absolute_path_for_doc_input_dir>

### Ollama Emulating Model Tag
# OLLAMA_EMULATING_MODEL_TAG=latest

### Max nodes return from grap retrieval
# MAX_GRAPH_NODES=1000

### Logging level
# LOG_LEVEL=INFO
# VERBOSE=False
# LOG_MAX_BYTES=10485760
# LOG_BACKUP_COUNT=5
### Logfile location (defaults to current working directory)
# LOG_DIR=/path/to/log/directory

### Settings for RAG query
# HISTORY_TURNS=3
# COSINE_THRESHOLD=0.2
# TOP_K=60
# MAX_TOKEN_TEXT_CHUNK=4000
# MAX_TOKEN_RELATION_DESC=4000
# MAX_TOKEN_ENTITY_DESC=4000

### Settings for document indexing
ENABLE_LLM_CACHE_FOR_EXTRACT=true
SUMMARY_LANGUAGE=English
# CHUNK_SIZE=1200
# CHUNK_OVERLAP_SIZE=100
### Max tokens for entity or relations summary
# MAX_TOKEN_SUMMARY=500
### Number of parallel processing documents in one patch
# MAX_PARALLEL_INSERT=2

### Num of chunks send to Embedding in single request
# EMBEDDING_BATCH_NUM=32
### Max concurrency requests for Embedding
# EMBEDDING_FUNC_MAX_ASYNC=16
# MAX_EMBED_TOKENS=8192

### LLM Configuration
### Time out in seconds for LLM, None for infinite timeout
TIMEOUT=150
### Some models like o1-mini require temperature to be set to 1
TEMPERATURE=0.5
### Max concurrency requests of LLM
MAX_ASYNC=4
### Max tokens send to LLM (less than context size of the model)
MAX_TOKENS=32768

### Ollama example (For local services installed with docker, you can use host.docker.internal as host)
LLM_BINDING=ollama
LLM_MODEL=mistral-nemo:latest
LLM_BINDING_API_KEY=your_api_key
LLM_BINDING_HOST=http://localhost:11434

### OpenAI alike example
# LLM_BINDING=openai
# LLM_MODEL=gpt-4o
# LLM_BINDING_HOST=https://api.openai.com/v1
# LLM_BINDING_API_KEY=your_api_key
### lollms example
# LLM_BINDING=lollms
# LLM_MODEL=mistral-nemo:latest
# LLM_BINDING_HOST=http://localhost:9600
# LLM_BINDING_API_KEY=your_api_key

### Embedding Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal)
EMBEDDING_MODEL=bge-m3:latest
EMBEDDING_DIM=1024
# EMBEDDING_BINDING_API_KEY=your_api_key
### ollama example
EMBEDDING_BINDING=ollama
EMBEDDING_BINDING_HOST=http://localhost:11434
### OpenAI alike example
# EMBEDDING_BINDING=openai
# LLM_BINDING_HOST=https://api.openai.com/v1
### Lollms example
# EMBEDDING_BINDING=lollms
# EMBEDDING_BINDING_HOST=http://localhost:9600

### Optional for Azure (LLM_BINDING_HOST, LLM_BINDING_API_KEY take priority)
# AZURE_OPENAI_API_VERSION=2024-08-01-preview
# AZURE_OPENAI_DEPLOYMENT=gpt-4o
# AZURE_OPENAI_API_KEY=your_api_key
# AZURE_OPENAI_ENDPOINT=https://myendpoint.openai.azure.com

# AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-large
# AZURE_EMBEDDING_API_VERSION=2023-05-15

### Data storage selection
LIGHTRAG_KV_STORAGE=JsonKVStorage
LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage
LIGHTRAG_GRAPH_STORAGE=NetworkXStorage
LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage

### TiDB Configuration (Deprecated)
# TIDB_HOST=localhost
# TIDB_PORT=4000
# TIDB_USER=your_username
# TIDB_PASSWORD='your_password'
# TIDB_DATABASE=your_database
### separating all data from difference Lightrag instances(deprecating)
# TIDB_WORKSPACE=default

### PostgreSQL Configuration
POSTGRES_HOST=localhost
POSTGRES_PORT=5432
POSTGRES_USER=your_username
POSTGRES_PASSWORD='your_password'
POSTGRES_DATABASE=your_database
### separating all data from difference Lightrag instances(deprecating)
# POSTGRES_WORKSPACE=default

### Independent AGM Configuration(not for AMG embedded in PostreSQL)
AGE_POSTGRES_DB=
AGE_POSTGRES_USER=
AGE_POSTGRES_PASSWORD=
AGE_POSTGRES_HOST=
# AGE_POSTGRES_PORT=8529

# AGE Graph Name(apply to PostgreSQL and independent AGM)
### AGE_GRAPH_NAME is precated
# AGE_GRAPH_NAME=lightrag

### Neo4j Configuration
NEO4J_URI=neo4j+s://xxxxxxxx.databases.neo4j.io
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD='your_password'

### MongoDB Configuration
MONGO_URI=mongodb://root:root@localhost:27017/
MONGO_DATABASE=LightRAG
### separating all data from difference Lightrag instances(deprecating)
# MONGODB_GRAPH=false

### Milvus Configuration
MILVUS_URI=http://localhost:19530
MILVUS_DB_NAME=lightrag
# MILVUS_USER=root
# MILVUS_PASSWORD=your_password
# MILVUS_TOKEN=your_token

### Qdrant
QDRANT_URL=http://localhost:16333
# QDRANT_API_KEY=your-api-key

### Redis
REDIS_URI=redis://localhost:6379

### For JWT Auth
# AUTH_ACCOUNTS='admin:admin123,user1:pass456'
# TOKEN_SECRET=Your-Key-For-LightRAG-API-Server
# TOKEN_EXPIRE_HOURS=48
# GUEST_TOKEN_EXPIRE_HOURS=24
# JWT_ALGORITHM=HS256

### API-Key to access LightRAG Server API
# LIGHTRAG_API_KEY=your-secure-api-key-here
# WHITELIST_PATHS=/health,/api/*